Chain of thought prompting in artificial intelligence refers to a technique used to improve the reasoning and problem-solving abilities of large language models (LLMs) by guiding them to break down complex tasks into smaller, sequential steps. Rather than asking the AI to generate an answer directly, chain of thought prompting encourages the model to explain its reasoning process step-by-step, which often leads to more accurate and reliable outputs. This method is particularly useful for tasks that require logical reasoning, mathematical calculations, or decision-making, where generating an immediate response might lead to errors or incomplete answers. The chain of thought approach leverages the model's capacity to follow a logical progression, ensuring that the final output is a result of a thorough, multi-step reasoning process.

The evolution of chain of thought prompting is closely tied to the development of increasingly sophisticated LLMs. Early language models focused primarily on generating text in response to single-turn prompts, without deep consideration for complex reasoning tasks. However, as LLMs grew in scale and capability, researchers discovered that these models could benefit from more structured guidance, especially for tasks requiring logic, multi-step calculations, or nuanced decision-making. Chain of thought prompting emerged as a solution, building on insights from cognitive science that breaking problems into steps can improve human reasoning. This approach was formalized with the rise of models like GPT-3 and GPT-4, where prompting strategies could significantly influence the quality of generated responses. Chain of thought prompting was first popularized as a way to guide AI into solving problems in a way that mirrors human thought processes, rather than simply relying on pattern recognition.

Prominent examples of chain of thought prompting include different techniques that fall under this category, such as “explicit prompting” and “implicit prompting.” Explicit prompting involves directly instructing the model to explain its reasoning before arriving at an answer. For example, a prompt might say: "Explain step by step how you would solve this math problem." Implicit prompting, on the other hand, doesn't explicitly ask the model to break down its reasoning but frames the task in a way that naturally leads the model to think through the problem. For example, giving a complex word problem that requires intermediate steps encourages the AI to perform these steps internally before arriving at the solution. These types of chain of thought prompting have demonstrated that even with minimal input modifications, LLMs can improve their problem-solving accuracy significantly by simulating a structured thought process.

The history of chain of thought prompting draws from both the fields of artificial intelligence and human cognitive psychology. The idea that step-by-step thinking can improve problem-solving has its roots in the 20th century, with figures like Allen Newell and Herbert A. Simon, who developed theories around problem-solving and AI that emphasized breaking down tasks into manageable components. These ideas later influenced the development of AI systems that focus on logical reasoning. In modern AI, chain of thought prompting became widely recognized through research conducted by OpenAI and other institutions, particularly as large language models like GPT-3 and GPT-4 showed that their reasoning abilities could be significantly enhanced with the right prompting strategies. Key names in this area include Jacob Andreas and his colleagues at MIT, who researched compositional reasoning, and researchers from OpenAI who have explored various prompt engineering methods.

Overall, chain of thought prompting is a technique designed to harness the latent reasoning capabilities of LLMs by guiding them through structured thought processes. It has evolved as LLMs have become more powerful, with prompting methods becoming more sophisticated and varied. This approach is particularly impactful in domains requiring logic, multi-step analysis, or precise decision-making, and continues to shape how AI is applied to complex reasoning tasks in industries like education, finance, and scientific research.

Here are some examples of Chain of Thought (CoT) prompting and related techniques used in AI to improve reasoning and problem-solving abilities:

1. Basic Chain of Thought Prompting Example

This is the most straightforward CoT approach, where the prompt explicitly asks the model to break down its reasoning process into clear steps before providing an answer.

Example: Question: "If there are 12 apples, and you give 3 apples to each of 4 friends, how many apples are left?"

Chain of Thought Prompt:"First, calculate how many apples you give away in total. Since you give 3 apples to 4 friends, multiply 3 by 4 to get 12. Next, subtract 12 apples from the total number of apples, which is 12. Therefore, 12 minus 12 equals 0. The answer is 0 apples are left."

Here, the AI is guided to follow a step-by-step reasoning process rather than directly answering the question.

2. Zero-shot Chain of Thought

This technique provides the model with a single prompt, and the model is expected to figure out the logical steps on its own, with no previous examples.

Example: Question: "John has 6 red balls and 4 blue balls. If he gives away 3 red balls, how many red balls does he have left?"

Zero-shot CoT Prompt:"John starts with 6 red balls. He gives away 3 red balls. To find out how many red balls are left, subtract 3 from 6, which gives 3. So, John has 3 red balls left."

3. Few-shot Chain of Thought Prompting

Few-shot CoT is a technique where the AI is provided with a few example problems (with both the question and the step-by-step reasoning provided) before being asked to solve a new problem. This method helps the AI model understand the structure of reasoning by learning from previous examples.

Example: Example 1: "If a train travels 60 miles per hour for 3 hours, how far has it traveled?" Step-by-step reasoning: "The train travels 60 miles in one hour. Multiply 60 by 3 to get 180 miles. So, the train has traveled 180 miles."

Example 2: "If a car drives at 50 miles per hour for 2 hours, how far has it traveled?" Step-by-step reasoning: "The car travels 50 miles in one hour. Multiply 50 by 2 to get 100 miles. So, the car has traveled 100 miles."

New Question: "If a bike rider pedals at 15 miles per hour for 4 hours, how far has the bike rider traveled?"

Few-shot CoT Prompt: "The bike rider travels 15 miles in one hour. Multiply 15 by 4 to get 60 miles. So, the bike rider has traveled 60 miles."

4. Multistep Reasoning Prompting

This approach breaks down more complex, multi-step reasoning problems, where the answer cannot be reached in one or two steps, such as solving logic puzzles or understanding word problems.

Example: Question: "Tom has twice as many marbles as Sally. Sally has 5 marbles. If Tom gives 3 marbles to Sally, how many marbles do they each have now?"

Multistep CoT Prompt: "Step 1: Tom has twice as many marbles as Sally, who has 5 marbles. So, Tom has 2 × 5 = 10 marbles. Step 2: Tom gives 3 marbles to Sally. Subtract 3 from Tom's 10 marbles to get 10 − 3 = 7 marbles. Step 3: Sally receives 3 marbles from Tom. Add 3 to Sally's 5 marbles to get 5 + 3 = 8 marbles. Final answer: Tom has 7 marbles, and Sally has 8 marbles."

5. Implicit Chain of Thought

In implicit CoT prompting, the model is not directly told to explain its thought process step-by-step, but the task naturally requires reasoning that involves multiple stages. The phrasing of the prompt encourages the AI to “think” through the problem without an explicit request for reasoning.

Example: Question: "There are 5 students. Each student has 4 notebooks. How many notebooks are there in total?"

Implicit CoT Prompt:"Each student has 4 notebooks, and there are 5 students. To find the total number of notebooks, multiply 5 by 4, which gives 20 notebooks in total."

Here, the AI model implicitly goes through the reasoning steps without explicitly being asked to do so.

6. Complex Problem-Solving CoT Prompt

For more difficult or abstract problems, CoT prompting can help the AI decompose complex questions into manageable parts.

Example: Question: "A factory produces 100 units of product A every hour. If it operates for 8 hours but loses 10% of its production to defects, how many usable units are produced in a day?"

Complex CoT Prompt:"Step 1: Calculate the total production without defects. The factory produces 100 units per hour, and it operates for 8 hours, so it produces 100 × 8 = 800 units in a day. Step 2: Calculate the number of defective units. Since 10% of the production is defective, multiply 800 by 0.10, which equals 80 defective units. Step 3: Subtract the defective units from the total production. 800 − 80 = 720 usable units. Final answer: The factory produces 720 usable units in a day."

These examples and techniques demonstrate how Chain of Thought (CoT) prompting improves a language model’s ability to process and solve tasks that require logical reasoning or multi-step analysis.